The Effect of Lifestyle on Willingness to Pay: Empirical Study of Organic Rice in Malang, Indonesia
Bibliographic record
Abstract
This study aims to analyze the relationship of lifestyle and general sustainability values to the level of Willingness To Pay for organic rice. This study used the Generalized Structured Component Analysis (GeSCA) method developed by Heungsun Hwang, Hec Montreal & Takane in 2004. The aim is to replace factors with linear combinations of indicators (manifest variables) in SEM analysis. This analysis approach uses the least square method in the parameter estimation process [1]. GeSCA is a new method of component-based SEM, very important and can be used for score calculation (not scale) and can also be applied to very small samples. The findings of the study based on the values of sustainability obtained altruistic values did not significantly influence on the value of the bio-sphere, biosphere values significantly influence on willingness to pay (WTP) and lifestyle significantly influence on altruistic values, and Willingness to Pay (WTP) for organic rice. This study is original because it focuses on certain regional areas in East Java Province, Indonesia. It concentrates on the problem of influences of lifestyle and the general sustainable value on consumer willingness to pay for organic rice so that it can provide marketers with information on how much they will pay more for organic rice. Therefore, this provides specific information that is important for journal readers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".